Methods › General › Control and Decision Systems › PPMC
Path Planning and Motion Control
PPMC
Introduced by Tamir Blum et al. in PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Path Planning and Motion Control, or PPMC RL, is a training algorithm that teaches path planning and motion control to robots using reinforcement learning in a simulated environment. The focus is on promoting generalization where there are environmental uncertainties such as rough environments like lunar services. The algorithm is coupled with any generic reinforcement learning algorithm to teach robots how to respond to user commands and to travel to designated locations on a single neural network. The algorithm works independently of the robot structure, demonstrating that it works on a wheeled rover in addition to the past results on a quadruped walking robot.
Papers archive 2025-07-28
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PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning 2 Mar 2020 · 1 repository · arXiv:2003.02655
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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